Precautions for study design and data interpretation of clinical metabolomics

Wang, Kaiqiang; Wang, Yuefei; Li, Kefeng

doi:10.1073/pnas.2118654119

Proc. Natl. Acad. Sci. U.S.A.

2022

DOI: 10.1073/pnas.2118654119

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Precautions for study design and data interpretation of clinical metabolomics

Kaiqiang Wang

Yuefei Wang

Kefeng Li

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Cited by 3 publications

(4 citation statements)

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“…Second, Zheng et al (2) note the age difference between test subjects with dementia and those who served as controls in our study (3). It is possible that aging increases the risk of neurodegenerative diseases (6).…”

mentioning

confidence: 64%

“…Accurate, nontargeted, comprehensive analysis of metabolites provides essential information about cutting-edge clinical questions (1). Zheng et al (2) question the legitimacy of our recent discovery of dementia markers using whole-blood metabolomics (3).…”

mentioning

confidence: 99%

“…Zheng et al. ( 2 ) question the legitimacy of our recent discovery of dementia markers using whole-blood metabolomics (3).…”

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confidence: 99%

“…First, they express concern about the sample size of our study ( n = 16) ( 2 ). However, our findings ( 3 ) do not conflict with other studies.…”

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confidence: 99%

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Reply to Zheng et al.: Clinical metabolomics: Detailed analysis by nontargeted method is complementary to large-scale studies

Kondoh

Teruya

Chen

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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confidence: 64%

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confidence: 99%

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Reply to Zheng et al.: Clinical metabolomics: Detailed analysis by nontargeted method is complementary to large-scale studies

Kondoh

Teruya

Chen

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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The emerging roles of next-generation metabolomics in critical care nutrition

Tong

Chen

et al. 2022

Critical Reviews in Food Science and Nutrition

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A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies

Shi¹,

Li²,

Zhang

et al. 2022

Metabolites

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As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLineTM and UlibMS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.

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Precautions for study design and data interpretation of clinical metabolomics

Cited by 3 publications

References 5 publications

Reply to Zheng et al.: Clinical metabolomics: Detailed analysis by nontargeted method is complementary to large-scale studies

Reply to Zheng et al.: Clinical metabolomics: Detailed analysis by nontargeted method is complementary to large-scale studies

The emerging roles of next-generation metabolomics in critical care nutrition

A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies

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